Overview

Dataset statistics

Number of variables41
Number of observations683788
Missing cells221365
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory213.9 MiB
Average record size in memory328.0 B

Variable types

Numeric14
DateTime1
Categorical11
Text6
Boolean9

Alerts

state has constant value ""Constant
block_id is highly overall correlated with borocode and 2 other fieldsHigh correlation
boro_ct is highly overall correlated with borocode and 7 other fieldsHigh correlation
borocode is highly overall correlated with block_id and 8 other fieldsHigh correlation
boroname is highly overall correlated with block_id and 4 other fieldsHigh correlation
cb_num is highly overall correlated with boro_ct and 7 other fieldsHigh correlation
cncldist is highly overall correlated with boro_ct and 7 other fieldsHigh correlation
guards is highly overall correlated with statusHigh correlation
health is highly overall correlated with statusHigh correlation
latitude is highly overall correlated with boro_ct and 7 other fieldsHigh correlation
longitude is highly overall correlated with cncldist and 7 other fieldsHigh correlation
sidewalk is highly overall correlated with statusHigh correlation
st_assem is highly overall correlated with longitude and 4 other fieldsHigh correlation
st_senate is highly overall correlated with boro_ct and 7 other fieldsHigh correlation
status is highly overall correlated with guards and 4 other fieldsHigh correlation
steward is highly overall correlated with statusHigh correlation
stump_diam is highly overall correlated with statusHigh correlation
x_sp is highly overall correlated with cncldist and 7 other fieldsHigh correlation
y_sp is highly overall correlated with boro_ct and 7 other fieldsHigh correlation
zip_city is highly overall correlated with block_id and 11 other fieldsHigh correlation
zipcode is highly overall correlated with longitude and 3 other fieldsHigh correlation
curb_loc is highly imbalanced (76.1%)Imbalance
status is highly imbalanced (80.1%)Imbalance
steward is highly imbalanced (51.7%)Imbalance
guards is highly imbalanced (65.6%)Imbalance
root_grate is highly imbalanced (95.3%)Imbalance
root_other is highly imbalanced (73.8%)Imbalance
trunk_wire is highly imbalanced (86.2%)Imbalance
trnk_light is highly imbalanced (98.4%)Imbalance
trnk_other is highly imbalanced (72.4%)Imbalance
brch_light is highly imbalanced (56.0%)Imbalance
brch_shoe is highly imbalanced (99.3%)Imbalance
brch_other is highly imbalanced (77.8%)Imbalance
health has 31616 (4.6%) missing valuesMissing
spc_latin has 31619 (4.6%) missing valuesMissing
spc_common has 31619 (4.6%) missing valuesMissing
steward has 31615 (4.6%) missing valuesMissing
guards has 31616 (4.6%) missing valuesMissing
sidewalk has 31616 (4.6%) missing valuesMissing
problems has 31664 (4.6%) missing valuesMissing
tree_id has unique valuesUnique
tree_dbh has 17932 (2.6%) zerosZeros
stump_diam has 666134 (97.4%) zerosZeros

Reproduction

Analysis started2023-12-13 14:23:17.568128
Analysis finished2023-12-13 14:38:30.198462
Duration15 minutes and 12.63 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

tree_id
Real number (ℝ)

UNIQUE 

Distinct683788
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365205.01
Minimum3
Maximum722694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:30.354888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile38056.35
Q1186582.75
median366214.5
Q3546170.25
95-th percentile687716.65
Maximum722694
Range722691
Interquartile range (IQR)359587.5

Descriptive statistics

Standard deviation208122.09
Coefficient of variation (CV)0.56987743
Kurtosis-1.192863
Mean365205.01
Median Absolute Deviation (MAD)179812.5
Skewness-0.017161241
Sum2.497228 × 1011
Variance4.3314806 × 1010
MonotonicityNot monotonic
2023-12-13T09:38:30.531186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606945 1
 
< 0.1%
179292 1
 
< 0.1%
467244 1
 
< 0.1%
21140 1
 
< 0.1%
348376 1
 
< 0.1%
266930 1
 
< 0.1%
644028 1
 
< 0.1%
86378 1
 
< 0.1%
527011 1
 
< 0.1%
529114 1
 
< 0.1%
Other values (683778) 683778
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
722694 1
< 0.1%
722693 1
< 0.1%
722692 1
< 0.1%
722691 1
< 0.1%
722690 1
< 0.1%
722689 1
< 0.1%
722688 1
< 0.1%
722687 1
< 0.1%
722686 1
< 0.1%
722685 1
< 0.1%

block_id
Real number (ℝ)

HIGH CORRELATION 

Distinct101390
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313793.1
Minimum100002
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:30.735215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum100002
5-th percentile107430
Q1221556
median319967
Q3404624
95-th percentile510007
Maximum999999
Range899997
Interquartile range (IQR)183068

Descriptive statistics

Standard deviation114839.02
Coefficient of variation (CV)0.36597053
Kurtosis-0.51238308
Mean313793.1
Median Absolute Deviation (MAD)91359
Skewness0.081632776
Sum2.1456795 × 1011
Variance1.3188002 × 1010
MonotonicityNot monotonic
2023-12-13T09:38:30.939506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204850 450
 
0.1%
602362 358
 
0.1%
208115 250
 
< 0.1%
506756 206
 
< 0.1%
233208 197
 
< 0.1%
340498 195
 
< 0.1%
111902 178
 
< 0.1%
302421 159
 
< 0.1%
501930 145
 
< 0.1%
340497 135
 
< 0.1%
Other values (101380) 681515
99.7%
ValueCountFrequency (%)
100002 4
 
< 0.1%
100003 14
< 0.1%
100004 3
 
< 0.1%
100005 4
 
< 0.1%
100014 2
 
< 0.1%
100015 5
 
< 0.1%
100016 5
 
< 0.1%
100018 5
 
< 0.1%
100019 2
 
< 0.1%
100028 5
 
< 0.1%
ValueCountFrequency (%)
999999 5
 
< 0.1%
603084 1
 
< 0.1%
603083 3
 
< 0.1%
603082 6
 
< 0.1%
603081 6
 
< 0.1%
603077 9
 
< 0.1%
603075 4
 
< 0.1%
603074 36
< 0.1%
603073 34
< 0.1%
603072 35
< 0.1%
Distinct483
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Minimum2015-05-19 00:00:00
Maximum2016-10-05 00:00:00
2023-12-13T09:38:31.382040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:31.548185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tree_dbh
Real number (ℝ)

ZEROS 

Distinct146
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279787
Minimum0
Maximum450
Zeros17932
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:31.720796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median9
Q316
95-th percentile28
Maximum450
Range450
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.7230423
Coefficient of variation (CV)0.77333395
Kurtosis46.977599
Mean11.279787
Median Absolute Deviation (MAD)5
Skewness2.4294724
Sum7712983
Variance76.091466
MonotonicityNot monotonic
2023-12-13T09:38:31.896644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 60372
 
8.8%
3 54454
 
8.0%
2 41977
 
6.1%
5 41642
 
6.1%
11 37978
 
5.6%
6 36519
 
5.3%
7 30862
 
4.5%
8 30828
 
4.5%
10 29672
 
4.3%
9 28903
 
4.2%
Other values (136) 290581
42.5%
ValueCountFrequency (%)
0 17932
 
2.6%
1 2899
 
0.4%
2 41977
6.1%
3 54454
8.0%
4 60372
8.8%
5 41642
6.1%
6 36519
5.3%
7 30862
4.5%
8 30828
4.5%
9 28903
4.2%
ValueCountFrequency (%)
450 1
< 0.1%
425 1
< 0.1%
389 1
< 0.1%
318 2
< 0.1%
298 1
< 0.1%
293 1
< 0.1%
291 1
< 0.1%
282 1
< 0.1%
281 1
< 0.1%
266 1
< 0.1%

stump_diam
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43246299
Minimum0
Maximum140
Zeros666134
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:32.101443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2902407
Coefficient of variation (CV)7.6081442
Kurtosis145.29814
Mean0.43246299
Median Absolute Deviation (MAD)0
Skewness10.36345
Sum295713
Variance10.825684
MonotonicityNot monotonic
2023-12-13T09:38:32.297034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 666134
97.4%
4 966
 
0.1%
5 939
 
0.1%
3 779
 
0.1%
6 754
 
0.1%
12 717
 
0.1%
10 716
 
0.1%
8 660
 
0.1%
14 660
 
0.1%
15 648
 
0.1%
Other values (90) 10815
 
1.6%
ValueCountFrequency (%)
0 666134
97.4%
1 106
 
< 0.1%
2 363
 
0.1%
3 779
 
0.1%
4 966
 
0.1%
5 939
 
0.1%
6 754
 
0.1%
7 612
 
0.1%
8 660
 
0.1%
9 530
 
0.1%
ValueCountFrequency (%)
140 1
< 0.1%
134 1
< 0.1%
131 1
< 0.1%
125 1
< 0.1%
120 1
< 0.1%
118 1
< 0.1%
115 1
< 0.1%
109 1
< 0.1%
107 1
< 0.1%
104 1
< 0.1%

curb_loc
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
OnCurb
656896 
OffsetFromCurb
 
26892

Length

Max length14
Median length6
Mean length6.3146238
Min length6

Characters and Unicode

Total characters4317864
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnCurb
2nd rowOnCurb
3rd rowOnCurb
4th rowOnCurb
5th rowOnCurb

Common Values

ValueCountFrequency (%)
OnCurb 656896
96.1%
OffsetFromCurb 26892
 
3.9%

Length

2023-12-13T09:38:32.530020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:32.736880image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
oncurb 656896
96.1%
offsetfromcurb 26892
 
3.9%

Most occurring characters

ValueCountFrequency (%)
r 710680
16.5%
O 683788
15.8%
C 683788
15.8%
u 683788
15.8%
b 683788
15.8%
n 656896
15.2%
f 53784
 
1.2%
s 26892
 
0.6%
e 26892
 
0.6%
t 26892
 
0.6%
Other values (3) 80676
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2923396
67.7%
Uppercase Letter 1394468
32.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 710680
24.3%
u 683788
23.4%
b 683788
23.4%
n 656896
22.5%
f 53784
 
1.8%
s 26892
 
0.9%
e 26892
 
0.9%
t 26892
 
0.9%
o 26892
 
0.9%
m 26892
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
O 683788
49.0%
C 683788
49.0%
F 26892
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4317864
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 710680
16.5%
O 683788
15.8%
C 683788
15.8%
u 683788
15.8%
b 683788
15.8%
n 656896
15.2%
f 53784
 
1.2%
s 26892
 
0.6%
e 26892
 
0.6%
t 26892
 
0.6%
Other values (3) 80676
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4317864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 710680
16.5%
O 683788
15.8%
C 683788
15.8%
u 683788
15.8%
b 683788
15.8%
n 656896
15.2%
f 53784
 
1.2%
s 26892
 
0.6%
e 26892
 
0.6%
t 26892
 
0.6%
Other values (3) 80676
 
1.9%

status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Alive
652173 
Stump
 
17654
Dead
 
13961

Length

Max length5
Median length5
Mean length4.9795829
Min length4

Characters and Unicode

Total characters3404979
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlive
2nd rowAlive
3rd rowAlive
4th rowAlive
5th rowAlive

Common Values

ValueCountFrequency (%)
Alive 652173
95.4%
Stump 17654
 
2.6%
Dead 13961
 
2.0%

Length

2023-12-13T09:38:32.912082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:33.104621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
alive 652173
95.4%
stump 17654
 
2.6%
dead 13961
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 666134
19.6%
A 652173
19.2%
l 652173
19.2%
i 652173
19.2%
v 652173
19.2%
S 17654
 
0.5%
t 17654
 
0.5%
u 17654
 
0.5%
m 17654
 
0.5%
p 17654
 
0.5%
Other values (3) 41883
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2721191
79.9%
Uppercase Letter 683788
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 666134
24.5%
l 652173
24.0%
i 652173
24.0%
v 652173
24.0%
t 17654
 
0.6%
u 17654
 
0.6%
m 17654
 
0.6%
p 17654
 
0.6%
a 13961
 
0.5%
d 13961
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 652173
95.4%
S 17654
 
2.6%
D 13961
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3404979
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 666134
19.6%
A 652173
19.2%
l 652173
19.2%
i 652173
19.2%
v 652173
19.2%
S 17654
 
0.5%
t 17654
 
0.5%
u 17654
 
0.5%
m 17654
 
0.5%
p 17654
 
0.5%
Other values (3) 41883
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3404979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 666134
19.6%
A 652173
19.2%
l 652173
19.2%
i 652173
19.2%
v 652173
19.2%
S 17654
 
0.5%
t 17654
 
0.5%
u 17654
 
0.5%
m 17654
 
0.5%
p 17654
 
0.5%
Other values (3) 41883
 
1.2%

health
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing31616
Missing (%)4.6%
Memory size5.2 MiB
Good
528850 
Fair
96504 
Poor
 
26818

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2608688
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good 528850
77.3%
Fair 96504
 
14.1%
Poor 26818
 
3.9%
(Missing) 31616
 
4.6%

Length

2023-12-13T09:38:33.262414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:33.465025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
good 528850
81.1%
fair 96504
 
14.8%
poor 26818
 
4.1%

Most occurring characters

ValueCountFrequency (%)
o 1111336
42.6%
G 528850
20.3%
d 528850
20.3%
r 123322
 
4.7%
F 96504
 
3.7%
a 96504
 
3.7%
i 96504
 
3.7%
P 26818
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1956516
75.0%
Uppercase Letter 652172
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1111336
56.8%
d 528850
27.0%
r 123322
 
6.3%
a 96504
 
4.9%
i 96504
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
G 528850
81.1%
F 96504
 
14.8%
P 26818
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2608688
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1111336
42.6%
G 528850
20.3%
d 528850
20.3%
r 123322
 
4.7%
F 96504
 
3.7%
a 96504
 
3.7%
i 96504
 
3.7%
P 26818
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2608688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1111336
42.6%
G 528850
20.3%
d 528850
20.3%
r 123322
 
4.7%
F 96504
 
3.7%
a 96504
 
3.7%
i 96504
 
3.7%
P 26818
 
1.0%

spc_latin
Text

MISSING 

Distinct132
Distinct (%)< 0.1%
Missing31619
Missing (%)4.6%
Memory size5.2 MiB
2023-12-13T09:38:33.762914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length34
Median length28
Mean length18.051819
Min length4

Characters and Unicode

Total characters11772837
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFraxinus pennsylvanica
2nd rowGleditsia triacanthos var. inermis
3rd rowPyrus calleryana
4th rowPyrus calleryana
5th rowPrunus virginiana
ValueCountFrequency (%)
acer 88739
 
6.0%
x 87130
 
5.9%
platanus 87014
 
5.9%
acerifolia 87014
 
5.9%
quercus 82867
 
5.6%
var 64605
 
4.4%
inermis 64605
 
4.4%
gleditsia 64264
 
4.3%
triacanthos 64264
 
4.3%
pyrus 58931
 
4.0%
Other values (168) 732098
49.4%
2023-12-13T09:38:34.374217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1560128
13.3%
i 1049571
 
8.9%
r 993219
 
8.4%
s 835821
 
7.1%
829362
 
7.0%
l 692257
 
5.9%
e 689988
 
5.9%
u 685213
 
5.8%
n 619786
 
5.3%
c 584030
 
5.0%
Other values (40) 3233462
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10203009
86.7%
Space Separator 829362
 
7.0%
Uppercase Letter 664015
 
5.6%
Other Punctuation 76451
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1560128
15.3%
i 1049571
10.3%
r 993219
9.7%
s 835821
8.2%
l 692257
 
6.8%
e 689988
 
6.8%
u 685213
 
6.7%
n 619786
 
6.1%
c 584030
 
5.7%
t 539091
 
5.3%
Other values (16) 1953905
19.2%
Uppercase Letter
ValueCountFrequency (%)
P 190226
28.6%
A 93024
14.0%
G 88652
13.4%
Q 82867
12.5%
T 53125
 
8.0%
Z 29258
 
4.4%
C 26340
 
4.0%
S 25213
 
3.8%
F 20379
 
3.1%
U 14915
 
2.2%
Other values (11) 40016
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 64605
84.5%
' 11846
 
15.5%
Space Separator
ValueCountFrequency (%)
829362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10867024
92.3%
Common 905813
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1560128
14.4%
i 1049571
9.7%
r 993219
 
9.1%
s 835821
 
7.7%
l 692257
 
6.4%
e 689988
 
6.3%
u 685213
 
6.3%
n 619786
 
5.7%
c 584030
 
5.4%
t 539091
 
5.0%
Other values (37) 2617920
24.1%
Common
ValueCountFrequency (%)
829362
91.6%
. 64605
 
7.1%
' 11846
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11772837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1560128
13.3%
i 1049571
 
8.9%
r 993219
 
8.4%
s 835821
 
7.1%
829362
 
7.0%
l 692257
 
5.9%
e 689988
 
5.9%
u 685213
 
5.8%
n 619786
 
5.3%
c 584030
 
5.0%
Other values (40) 3233462
27.5%

spc_common
Text

MISSING 

Distinct132
Distinct (%)< 0.1%
Missing31619
Missing (%)4.6%
Memory size5.2 MiB
2023-12-13T09:38:34.758778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length11.968832
Min length3

Characters and Unicode

Total characters7805701
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgreen ash
2nd rowhoneylocust
3rd rowCallery pear
4th rowCallery pear
5th row'Schubert' chokecherry
ValueCountFrequency (%)
maple 88675
 
7.6%
london 87014
 
7.4%
planetree 87014
 
7.4%
oak 82867
 
7.1%
honeylocust 64264
 
5.5%
callery 58931
 
5.0%
pear 58931
 
5.0%
pin 53185
 
4.6%
linden 51267
 
4.4%
japanese 35774
 
3.1%
Other values (135) 500206
42.8%
2023-12-13T09:38:35.422489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1090341
14.0%
a 697628
 
8.9%
n 661863
 
8.5%
l 632093
 
8.1%
o 590406
 
7.6%
r 548056
 
7.0%
515959
 
6.6%
p 390862
 
5.0%
t 289664
 
3.7%
i 261377
 
3.3%
Other values (32) 2127452
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6981119
89.4%
Space Separator 515959
 
6.6%
Uppercase Letter 289070
 
3.7%
Other Punctuation 11263
 
0.1%
Dash Punctuation 8290
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1090341
15.6%
a 697628
10.0%
n 661863
9.5%
l 632093
 
9.1%
o 590406
 
8.5%
r 548056
 
7.9%
p 390862
 
5.6%
t 289664
 
4.1%
i 261377
 
3.7%
y 209494
 
3.0%
Other values (15) 1609335
23.1%
Uppercase Letter
ValueCountFrequency (%)
L 87014
30.1%
C 66211
22.9%
J 35774
12.4%
N 34544
 
12.0%
A 29293
 
10.1%
S 27392
 
9.5%
E 3915
 
1.4%
K 3843
 
1.3%
O 323
 
0.1%
T 317
 
0.1%
Other values (4) 444
 
0.2%
Space Separator
ValueCountFrequency (%)
515959
100.0%
Other Punctuation
ValueCountFrequency (%)
' 11263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7270189
93.1%
Common 535512
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1090341
15.0%
a 697628
 
9.6%
n 661863
 
9.1%
l 632093
 
8.7%
o 590406
 
8.1%
r 548056
 
7.5%
p 390862
 
5.4%
t 289664
 
4.0%
i 261377
 
3.6%
y 209494
 
2.9%
Other values (29) 1898405
26.1%
Common
ValueCountFrequency (%)
515959
96.3%
' 11263
 
2.1%
- 8290
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7805701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1090341
14.0%
a 697628
 
8.9%
n 661863
 
8.5%
l 632093
 
8.1%
o 590406
 
7.6%
r 548056
 
7.0%
515959
 
6.6%
p 390862
 
5.0%
t 289664
 
3.7%
i 261377
 
3.3%
Other values (32) 2127452
27.3%

steward
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing31615
Missing (%)4.6%
Memory size5.2 MiB
None
487823 
1or2
143557 
3or4
 
19183
4orMore
 
1610

Length

Max length7
Median length4
Mean length4.007406
Min length4

Characters and Unicode

Total characters2613522
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 487823
71.3%
1or2 143557
 
21.0%
3or4 19183
 
2.8%
4orMore 1610
 
0.2%
(Missing) 31615
 
4.6%

Length

2023-12-13T09:38:35.650019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:35.908110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
none 487823
74.8%
1or2 143557
 
22.0%
3or4 19183
 
2.9%
4ormore 1610
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 653783
25.0%
e 489433
18.7%
N 487823
18.7%
n 487823
18.7%
r 165960
 
6.4%
1 143557
 
5.5%
2 143557
 
5.5%
4 20793
 
0.8%
3 19183
 
0.7%
M 1610
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1796999
68.8%
Uppercase Letter 489433
 
18.7%
Decimal Number 327090
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 653783
36.4%
e 489433
27.2%
n 487823
27.1%
r 165960
 
9.2%
Decimal Number
ValueCountFrequency (%)
1 143557
43.9%
2 143557
43.9%
4 20793
 
6.4%
3 19183
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
N 487823
99.7%
M 1610
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2286432
87.5%
Common 327090
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 653783
28.6%
e 489433
21.4%
N 487823
21.3%
n 487823
21.3%
r 165960
 
7.3%
M 1610
 
0.1%
Common
ValueCountFrequency (%)
1 143557
43.9%
2 143557
43.9%
4 20793
 
6.4%
3 19183
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2613522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 653783
25.0%
e 489433
18.7%
N 487823
18.7%
n 487823
18.7%
r 165960
 
6.4%
1 143557
 
5.5%
2 143557
 
5.5%
4 20793
 
0.8%
3 19183
 
0.7%
M 1610
 
0.1%

guards
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing31616
Missing (%)4.6%
Memory size5.2 MiB
None
572306 
Helpful
 
51866
Harmful
 
20252
Unsure
 
7748

Length

Max length7
Median length4
Mean length4.3555044
Min length4

Characters and Unicode

Total characters2840538
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 572306
83.7%
Helpful 51866
 
7.6%
Harmful 20252
 
3.0%
Unsure 7748
 
1.1%
(Missing) 31616
 
4.6%

Length

2023-12-13T09:38:36.119683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:36.315244image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
none 572306
87.8%
helpful 51866
 
8.0%
harmful 20252
 
3.1%
unsure 7748
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 631920
22.2%
n 580054
20.4%
N 572306
20.1%
o 572306
20.1%
l 123984
 
4.4%
u 79866
 
2.8%
H 72118
 
2.5%
f 72118
 
2.5%
p 51866
 
1.8%
r 28000
 
1.0%
Other values (4) 56000
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2188366
77.0%
Uppercase Letter 652172
 
23.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 631920
28.9%
n 580054
26.5%
o 572306
26.2%
l 123984
 
5.7%
u 79866
 
3.6%
f 72118
 
3.3%
p 51866
 
2.4%
r 28000
 
1.3%
a 20252
 
0.9%
m 20252
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
N 572306
87.8%
H 72118
 
11.1%
U 7748
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2840538
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 631920
22.2%
n 580054
20.4%
N 572306
20.1%
o 572306
20.1%
l 123984
 
4.4%
u 79866
 
2.8%
H 72118
 
2.5%
f 72118
 
2.5%
p 51866
 
1.8%
r 28000
 
1.0%
Other values (4) 56000
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2840538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 631920
22.2%
n 580054
20.4%
N 572306
20.1%
o 572306
20.1%
l 123984
 
4.4%
u 79866
 
2.8%
H 72118
 
2.5%
f 72118
 
2.5%
p 51866
 
1.8%
r 28000
 
1.0%
Other values (4) 56000
 
2.0%

sidewalk
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing31616
Missing (%)4.6%
Memory size5.2 MiB
NoDamage
464978 
Damage
187194 

Length

Max length8
Median length8
Mean length7.4259367
Min length6

Characters and Unicode

Total characters4842988
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoDamage
2nd rowNoDamage
3rd rowNoDamage
4th rowNoDamage
5th rowNoDamage

Common Values

ValueCountFrequency (%)
NoDamage 464978
68.0%
Damage 187194
27.4%
(Missing) 31616
 
4.6%

Length

2023-12-13T09:38:36.531025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:36.771838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
nodamage 464978
71.3%
damage 187194
28.7%

Most occurring characters

ValueCountFrequency (%)
a 1304344
26.9%
D 652172
13.5%
m 652172
13.5%
g 652172
13.5%
e 652172
13.5%
N 464978
 
9.6%
o 464978
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3725838
76.9%
Uppercase Letter 1117150
 
23.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1304344
35.0%
m 652172
17.5%
g 652172
17.5%
e 652172
17.5%
o 464978
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 652172
58.4%
N 464978
41.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4842988
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1304344
26.9%
D 652172
13.5%
m 652172
13.5%
g 652172
13.5%
e 652172
13.5%
N 464978
 
9.6%
o 464978
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1304344
26.9%
D 652172
13.5%
m 652172
13.5%
g 652172
13.5%
e 652172
13.5%
N 464978
 
9.6%
o 464978
 
9.6%

user_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
TreesCount Staff
296284 
Volunteer
217518 
NYC Parks Staff
169986 

Length

Max length16
Median length15
Mean length13.524654
Min length9

Characters and Unicode

Total characters9247996
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTreesCount Staff
2nd rowVolunteer
3rd rowTreesCount Staff
4th rowTreesCount Staff
5th rowTreesCount Staff

Common Values

ValueCountFrequency (%)
TreesCount Staff 296284
43.3%
Volunteer 217518
31.8%
NYC Parks Staff 169986
24.9%

Length

2023-12-13T09:38:36.931788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:37.140403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
staff 466270
35.3%
treescount 296284
22.4%
volunteer 217518
16.5%
nyc 169986
 
12.9%
parks 169986
 
12.9%

Most occurring characters

ValueCountFrequency (%)
e 1027604
11.1%
t 980072
10.6%
f 932540
 
10.1%
r 683788
 
7.4%
636256
 
6.9%
a 636256
 
6.9%
o 513802
 
5.6%
u 513802
 
5.6%
n 513802
 
5.6%
s 466270
 
5.0%
Other values (9) 2343804
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6655440
72.0%
Uppercase Letter 1956300
 
21.2%
Space Separator 636256
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1027604
15.4%
t 980072
14.7%
f 932540
14.0%
r 683788
10.3%
a 636256
9.6%
o 513802
7.7%
u 513802
7.7%
n 513802
7.7%
s 466270
7.0%
l 217518
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 466270
23.8%
S 466270
23.8%
T 296284
15.1%
V 217518
11.1%
N 169986
 
8.7%
Y 169986
 
8.7%
P 169986
 
8.7%
Space Separator
ValueCountFrequency (%)
636256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8611740
93.1%
Common 636256
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1027604
11.9%
t 980072
11.4%
f 932540
10.8%
r 683788
 
7.9%
a 636256
 
7.4%
o 513802
 
6.0%
u 513802
 
6.0%
n 513802
 
6.0%
s 466270
 
5.4%
C 466270
 
5.4%
Other values (8) 1877534
21.8%
Common
ValueCountFrequency (%)
636256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9247996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1027604
11.1%
t 980072
10.6%
f 932540
 
10.1%
r 683788
 
7.4%
636256
 
6.9%
a 636256
 
6.9%
o 513802
 
5.6%
u 513802
 
5.6%
n 513802
 
5.6%
s 466270
 
5.0%
Other values (9) 2343804
25.3%

problems
Text

MISSING 

Distinct232
Distinct (%)< 0.1%
Missing31664
Missing (%)4.6%
Memory size5.2 MiB
2023-12-13T09:38:37.351395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length87
Median length4
Mean length6.6444695
Min length4

Characters and Unicode

Total characters4333018
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st rowStones
2nd rowBranchLights
3rd rowBranchLights
4th rowNone
5th rowBranchLights
ValueCountFrequency (%)
none 426280
65.4%
stones 95673
 
14.7%
branchlights 29452
 
4.5%
stonesbranchlights 17808
 
2.7%
rootother 11418
 
1.8%
trunkother 11143
 
1.7%
branchother 8352
 
1.3%
stonestrunkother 5183
 
0.8%
stonesrootother 4468
 
0.7%
wiresrope 4095
 
0.6%
Other values (222) 38252
 
5.9%
2023-12-13T09:38:37.818107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 687971
15.9%
n 687014
15.9%
o 640197
14.8%
N 426280
9.8%
t 328039
7.6%
h 237366
 
5.5%
r 224795
 
5.2%
s 220616
 
5.1%
S 140410
 
3.2%
a 94203
 
2.2%
Other values (15) 646127
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3431416
79.2%
Uppercase Letter 901602
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 687971
20.0%
n 687014
20.0%
o 640197
18.7%
t 328039
9.6%
h 237366
 
6.9%
r 224795
 
6.6%
s 220616
 
6.4%
a 94203
 
2.7%
c 86720
 
2.5%
i 76670
 
2.2%
Other values (5) 147825
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
N 426280
47.3%
S 140410
 
15.6%
O 87250
 
9.7%
B 86720
 
9.6%
L 63396
 
7.0%
R 43596
 
4.8%
T 33604
 
3.7%
W 13274
 
1.5%
M 3536
 
0.4%
G 3536
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4333018
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 687971
15.9%
n 687014
15.9%
o 640197
14.8%
N 426280
9.8%
t 328039
7.6%
h 237366
 
5.5%
r 224795
 
5.2%
s 220616
 
5.1%
S 140410
 
3.2%
a 94203
 
2.2%
Other values (15) 646127
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4333018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 687971
15.9%
n 687014
15.9%
o 640197
14.8%
N 426280
9.8%
t 328039
7.6%
h 237366
 
5.5%
r 224795
 
5.2%
s 220616
 
5.1%
S 140410
 
3.2%
a 94203
 
2.2%
Other values (15) 646127
14.9%

root_stone
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
543789 
True
139999 
ValueCountFrequency (%)
False 543789
79.5%
True 139999
 
20.5%
2023-12-13T09:38:38.041538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

root_grate
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
680252 
True
 
3536
ValueCountFrequency (%)
False 680252
99.5%
True 3536
 
0.5%
2023-12-13T09:38:38.212327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

root_other
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
653466 
True
 
30322
ValueCountFrequency (%)
False 653466
95.6%
True 30322
 
4.4%
2023-12-13T09:38:38.366673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

trunk_wire
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
670514 
True
 
13274
ValueCountFrequency (%)
False 670514
98.1%
True 13274
 
1.9%
2023-12-13T09:38:38.550374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

trnk_light
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
682757 
True
 
1031
ValueCountFrequency (%)
False 682757
99.8%
True 1031
 
0.2%
2023-12-13T09:38:38.724049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

trnk_other
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
651215 
True
 
32573
ValueCountFrequency (%)
False 651215
95.2%
True 32573
 
4.8%
2023-12-13T09:38:38.894320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

brch_light
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
621423 
True
62365 
ValueCountFrequency (%)
False 621423
90.9%
True 62365
 
9.1%
2023-12-13T09:38:39.096785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

brch_shoe
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
683377 
True
 
411
ValueCountFrequency (%)
False 683377
99.9%
True 411
 
0.1%
2023-12-13T09:38:39.268986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

brch_other
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.9 KiB
False
659433 
True
 
24355
ValueCountFrequency (%)
False 659433
96.4%
True 24355
 
3.6%
2023-12-13T09:38:39.447545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Distinct408701
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:39.856661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length40
Median length38
Mean length18.023022
Min length1

Characters and Unicode

Total characters12323926
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275576 ?
Unique (%)40.3%

Sample

1st row76-046 164 STREET
2nd row72-020 32 AVENUE
3rd row153-026 119 AVENUE
4th row89 89 STREET
5th row559 BEACH 68 STREET
ValueCountFrequency (%)
street 294164
 
13.4%
avenue 256523
 
11.7%
east 56810
 
2.6%
road 32465
 
1.5%
west 28418
 
1.3%
boulevard 26564
 
1.2%
place 24797
 
1.1%
parkway 12442
 
0.6%
drive 10092
 
0.5%
beach 7148
 
0.3%
Other values (29903) 1438138
65.7%
2023-12-13T09:38:40.597771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1564995
 
12.7%
1503773
 
12.2%
T 847317
 
6.9%
A 680093
 
5.5%
R 632291
 
5.1%
1 625603
 
5.1%
0 548667
 
4.5%
S 538560
 
4.4%
N 506195
 
4.1%
2 422115
 
3.4%
Other values (29) 4454317
36.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7205359
58.5%
Decimal Number 3375683
27.4%
Space Separator 1503773
 
12.2%
Dash Punctuation 238924
 
1.9%
Other Punctuation 187
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1564995
21.7%
T 847317
11.8%
A 680093
9.4%
R 632291
8.8%
S 538560
 
7.5%
N 506195
 
7.0%
U 352144
 
4.9%
V 324424
 
4.5%
O 290207
 
4.0%
L 239144
 
3.3%
Other values (16) 1229989
17.1%
Decimal Number
ValueCountFrequency (%)
1 625603
18.5%
0 548667
16.3%
2 422115
12.5%
3 307178
9.1%
4 297043
8.8%
5 287307
8.5%
6 245067
 
7.3%
7 225537
 
6.7%
8 221016
 
6.5%
9 196150
 
5.8%
Space Separator
ValueCountFrequency (%)
1503773
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 238924
100.0%
Other Punctuation
ValueCountFrequency (%)
' 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7205359
58.5%
Common 5118567
41.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1564995
21.7%
T 847317
11.8%
A 680093
9.4%
R 632291
8.8%
S 538560
 
7.5%
N 506195
 
7.0%
U 352144
 
4.9%
V 324424
 
4.5%
O 290207
 
4.0%
L 239144
 
3.3%
Other values (16) 1229989
17.1%
Common
ValueCountFrequency (%)
1503773
29.4%
1 625603
12.2%
0 548667
 
10.7%
2 422115
 
8.2%
3 307178
 
6.0%
4 297043
 
5.8%
5 287307
 
5.6%
6 245067
 
4.8%
- 238924
 
4.7%
7 225537
 
4.4%
Other values (3) 417353
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12323926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1564995
 
12.7%
1503773
 
12.2%
T 847317
 
6.9%
A 680093
 
5.5%
R 632291
 
5.1%
1 625603
 
5.1%
0 548667
 
4.5%
S 538560
 
4.4%
N 506195
 
4.1%
2 422115
 
3.4%
Other values (29) 4454317
36.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10916.246
Minimum83
Maximum11697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:40.867231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile10025
Q110451
median11214
Q311365
95-th percentile11432
Maximum11697
Range11614
Interquartile range (IQR)914

Descriptive statistics

Standard deviation651.55336
Coefficient of variation (CV)0.059686577
Kurtosis102.1138
Mean10916.246
Median Absolute Deviation (MAD)203
Skewness-6.5077556
Sum7.464398 × 109
Variance424521.79
MonotonicityNot monotonic
2023-12-13T09:38:41.332138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10312 22186
 
3.2%
10314 16905
 
2.5%
10306 13030
 
1.9%
10309 12650
 
1.8%
11234 11253
 
1.6%
11385 10937
 
1.6%
11357 9449
 
1.4%
11207 8634
 
1.3%
11434 8274
 
1.2%
11208 8245
 
1.2%
Other values (181) 562225
82.2%
ValueCountFrequency (%)
83 935
0.1%
10001 911
0.1%
10002 2265
0.3%
10003 2025
0.3%
10004 118
 
< 0.1%
10005 144
 
< 0.1%
10006 53
 
< 0.1%
10007 355
 
0.1%
10009 1924
0.3%
10010 889
 
0.1%
ValueCountFrequency (%)
11697 30
 
< 0.1%
11694 3572
0.5%
11693 1169
 
0.2%
11692 2013
 
0.3%
11691 5718
0.8%
11451 12
 
< 0.1%
11436 2407
 
0.4%
11435 4595
0.7%
11434 8274
1.2%
11433 3745
0.5%

zip_city
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Brooklyn
177300 
Staten Island
105318 
Bronx
85203 
New York
64488 
Jamaica
26028 
Other values (43)
225451 

Length

Max length19
Median length16
Mean length9.3159605
Min length5

Characters and Unicode

Total characters6370142
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFresh Meadows
2nd rowEast Elmhurst
3rd rowJamaica
4th rowBrooklyn
5th rowArverne

Common Values

ValueCountFrequency (%)
Brooklyn 177300
25.9%
Staten Island 105318
15.4%
Bronx 85203
12.5%
New York 64488
 
9.4%
Jamaica 26028
 
3.8%
Flushing 23389
 
3.4%
Ridgewood 10937
 
1.6%
Fresh Meadows 10441
 
1.5%
Queens Village 10127
 
1.5%
Astoria 10007
 
1.5%
Other values (38) 160550
23.5%

Length

2023-12-13T09:38:41.497947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brooklyn 177300
17.9%
island 108797
 
11.0%
staten 105318
 
10.6%
bronx 85203
 
8.6%
new 65353
 
6.6%
york 64488
 
6.5%
jamaica 26028
 
2.6%
flushing 23389
 
2.4%
park 20945
 
2.1%
gardens 16267
 
1.6%
Other values (52) 296328
29.9%

Most occurring characters

ValueCountFrequency (%)
o 662058
 
10.4%
n 604433
 
9.5%
a 474555
 
7.4%
l 446198
 
7.0%
r 440622
 
6.9%
e 400969
 
6.3%
t 309410
 
4.9%
305628
 
4.8%
k 294951
 
4.6%
s 283899
 
4.5%
Other values (36) 2147419
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5075098
79.7%
Uppercase Letter 989416
 
15.5%
Space Separator 305628
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 662058
13.0%
n 604433
11.9%
a 474555
9.4%
l 446198
8.8%
r 440622
8.7%
e 400969
7.9%
t 309410
 
6.1%
k 294951
 
5.8%
s 283899
 
5.6%
d 224233
 
4.4%
Other values (14) 933770
18.4%
Uppercase Letter
ValueCountFrequency (%)
B 280983
28.4%
S 127448
12.9%
I 108806
 
11.0%
N 72633
 
7.3%
Y 64488
 
6.5%
F 49315
 
5.0%
R 37000
 
3.7%
J 29323
 
3.0%
H 28885
 
2.9%
P 24074
 
2.4%
Other values (11) 166461
16.8%
Space Separator
ValueCountFrequency (%)
305628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6064514
95.2%
Common 305628
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 662058
 
10.9%
n 604433
 
10.0%
a 474555
 
7.8%
l 446198
 
7.4%
r 440622
 
7.3%
e 400969
 
6.6%
t 309410
 
5.1%
k 294951
 
4.9%
s 283899
 
4.7%
B 280983
 
4.6%
Other values (35) 1866436
30.8%
Common
ValueCountFrequency (%)
305628
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6370142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 662058
 
10.4%
n 604433
 
9.5%
a 474555
 
7.4%
l 446198
 
7.0%
r 440622
 
6.9%
e 400969
 
6.3%
t 309410
 
4.9%
305628
 
4.8%
k 294951
 
4.6%
s 283899
 
4.5%
Other values (36) 2147419
33.7%

cb_num
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.5054
Minimum101
Maximum503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:41.701384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile108
Q1302
median402
Q3412
95-th percentile503
Maximum503
Range402
Interquartile range (IQR)110

Descriptive statistics

Standard deviation115.7406
Coefficient of variation (CV)0.33693968
Kurtosis-0.51111783
Mean343.5054
Median Absolute Deviation (MAD)92
Skewness-0.56103898
Sum2.3488487 × 108
Variance13395.887
MonotonicityNot monotonic
2023-12-13T09:38:41.930120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503 53934
 
7.9%
413 37017
 
5.4%
407 30620
 
4.5%
411 28071
 
4.1%
412 26379
 
3.9%
502 25717
 
3.8%
501 25667
 
3.8%
408 20383
 
3.0%
405 19550
 
2.9%
318 19319
 
2.8%
Other values (49) 397131
58.1%
ValueCountFrequency (%)
101 2397
 
0.4%
102 5019
0.7%
103 4939
0.7%
104 4704
0.7%
105 2156
 
0.3%
106 5061
0.7%
107 8814
1.3%
108 9269
1.4%
109 4987
0.7%
110 5962
0.9%
ValueCountFrequency (%)
503 53934
7.9%
502 25717
3.8%
501 25667
3.8%
414 12412
 
1.8%
413 37017
5.4%
412 26379
3.9%
411 28071
4.1%
410 15224
 
2.2%
409 11481
 
1.7%
408 20383
 
3.0%

borocode
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
4
250551 
3
177293 
5
105318 
2
85203 
1
65423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters683788
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

Length

2023-12-13T09:38:42.152697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:42.389813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

Most occurring characters

ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 683788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 683788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 683788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 250551
36.6%
3 177293
25.9%
5 105318
15.4%
2 85203
 
12.5%
1 65423
 
9.6%

boroname
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Queens
250551 
Brooklyn
177293 
Staten Island
105318 
Bronx
85203 
Manhattan
65423 

Length

Max length13
Median length9
Mean length7.7591388
Min length5

Characters and Unicode

Total characters5305606
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowQueens
3rd rowQueens
4th rowBrooklyn
5th rowQueens

Common Values

ValueCountFrequency (%)
Queens 250551
36.6%
Brooklyn 177293
25.9%
Staten Island 105318
15.4%
Bronx 85203
 
12.5%
Manhattan 65423
 
9.6%

Length

2023-12-13T09:38:42.602353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:42.809886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
queens 250551
31.8%
brooklyn 177293
22.5%
staten 105318
13.3%
island 105318
13.3%
bronx 85203
 
10.8%
manhattan 65423
 
8.3%

Most occurring characters

ValueCountFrequency (%)
n 854529
16.1%
e 606420
11.4%
o 439789
 
8.3%
a 406905
 
7.7%
s 355869
 
6.7%
t 341482
 
6.4%
l 282611
 
5.3%
B 262496
 
4.9%
r 262496
 
4.9%
Q 250551
 
4.7%
Other values (10) 1242458
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4411182
83.1%
Uppercase Letter 789106
 
14.9%
Space Separator 105318
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 854529
19.4%
e 606420
13.7%
o 439789
10.0%
a 406905
9.2%
s 355869
8.1%
t 341482
 
7.7%
l 282611
 
6.4%
r 262496
 
6.0%
u 250551
 
5.7%
y 177293
 
4.0%
Other values (4) 433237
9.8%
Uppercase Letter
ValueCountFrequency (%)
B 262496
33.3%
Q 250551
31.8%
S 105318
13.3%
I 105318
13.3%
M 65423
 
8.3%
Space Separator
ValueCountFrequency (%)
105318
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5200288
98.0%
Common 105318
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 854529
16.4%
e 606420
11.7%
o 439789
 
8.5%
a 406905
 
7.8%
s 355869
 
6.8%
t 341482
 
6.6%
l 282611
 
5.4%
B 262496
 
5.0%
r 262496
 
5.0%
Q 250551
 
4.8%
Other values (9) 1137140
21.9%
Common
ValueCountFrequency (%)
105318
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5305606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 854529
16.1%
e 606420
11.4%
o 439789
 
8.3%
a 406905
 
7.7%
s 355869
 
6.7%
t 341482
 
6.4%
l 282611
 
5.3%
B 262496
 
4.9%
r 262496
 
4.9%
Q 250551
 
4.7%
Other values (10) 1242458
23.4%

cncldist
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.943181
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:43.019121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q119
median30
Q343
95-th percentile51
Maximum51
Range50
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.328531
Coefficient of variation (CV)0.47852401
Kurtosis-1.0505651
Mean29.943181
Median Absolute Deviation (MAD)12
Skewness-0.10899332
Sum20474788
Variance205.30681
MonotonicityNot monotonic
2023-12-13T09:38:43.269666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 51236
 
7.5%
19 34429
 
5.0%
50 33035
 
4.8%
23 30743
 
4.5%
31 23161
 
3.4%
49 21047
 
3.1%
27 20116
 
2.9%
32 19508
 
2.9%
24 18993
 
2.8%
30 18551
 
2.7%
Other values (41) 412969
60.4%
ValueCountFrequency (%)
1 5694
0.8%
2 5564
0.8%
3 8631
1.3%
4 8521
1.2%
5 4982
0.7%
6 8050
1.2%
7 6572
1.0%
8 7293
1.1%
9 8213
1.2%
10 6501
1.0%
ValueCountFrequency (%)
51 51236
7.5%
50 33035
4.8%
49 21047
3.1%
48 11786
 
1.7%
47 9259
 
1.4%
46 16913
 
2.5%
45 11758
 
1.7%
44 11659
 
1.7%
43 13196
 
1.9%
42 13117
 
1.9%

st_assem
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.791583
Minimum23
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:43.474488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q133
median52
Q364
95-th percentile83
Maximum87
Range64
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.96652
Coefficient of variation (CV)0.37341856
Kurtosis-1.1675283
Mean50.791583
Median Absolute Deviation (MAD)16
Skewness0.15900702
Sum34730675
Variance359.72888
MonotonicityNot monotonic
2023-12-13T09:38:43.717750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 46002
 
6.7%
26 27763
 
4.1%
64 22922
 
3.4%
63 21883
 
3.2%
25 21514
 
3.1%
33 20927
 
3.1%
61 17669
 
2.6%
24 17643
 
2.6%
23 17615
 
2.6%
29 17572
 
2.6%
Other values (55) 452278
66.1%
ValueCountFrequency (%)
23 17615
2.6%
24 17643
2.6%
25 21514
3.1%
26 27763
4.1%
27 14853
2.2%
28 13789
2.0%
29 17572
2.6%
30 11689
1.7%
31 14451
2.1%
32 13443
2.0%
ValueCountFrequency (%)
87 7428
1.1%
86 5530
0.8%
85 6543
1.0%
84 8253
1.2%
83 8808
1.3%
82 12887
1.9%
81 8537
1.2%
80 9314
1.4%
79 7083
1.0%
78 5182
0.8%

st_senate
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.615781
Minimum10
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:43.938642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q114
median21
Q325
95-th percentile34
Maximum36
Range26
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3908438
Coefficient of variation (CV)0.35850418
Kurtosis-0.94839788
Mean20.615781
Median Absolute Deviation (MAD)6
Skewness0.29937187
Sum14096824
Variance54.624573
MonotonicityNot monotonic
2023-12-13T09:38:44.114171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
24 87241
 
12.8%
11 67705
 
9.9%
15 44624
 
6.5%
14 38351
 
5.6%
10 35044
 
5.1%
34 31066
 
4.5%
19 29083
 
4.3%
22 27179
 
4.0%
23 25401
 
3.7%
16 24146
 
3.5%
Other values (16) 273948
40.1%
ValueCountFrequency (%)
10 35044
5.1%
11 67705
9.9%
12 21847
 
3.2%
13 18827
 
2.8%
14 38351
5.6%
15 44624
6.5%
16 24146
 
3.5%
17 22225
 
3.3%
18 20603
 
3.0%
19 29083
4.3%
ValueCountFrequency (%)
36 14544
2.1%
34 31066
4.5%
33 14462
2.1%
32 15915
2.3%
31 12898
1.9%
30 13929
2.0%
29 14285
2.1%
28 13195
1.9%
27 13685
2.0%
26 16456
2.4%

nta
Text

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:44.594513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2735152
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQN37
2nd rowQN28
3rd rowQN76
4th rowBK31
5th rowQN12
ValueCountFrequency (%)
si01 12969
 
1.9%
si54 10734
 
1.6%
qn46 9780
 
1.4%
bk82 9607
 
1.4%
si32 9251
 
1.4%
si05 8446
 
1.2%
si11 8216
 
1.2%
qn17 7701
 
1.1%
qn49 7620
 
1.1%
bk45 7449
 
1.1%
Other values (178) 592015
86.6%
2023-12-13T09:38:45.104701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 316193
11.6%
B 262277
 
9.6%
Q 250524
 
9.2%
3 187685
 
6.9%
2 182588
 
6.7%
K 177320
 
6.5%
4 174978
 
6.4%
1 167614
 
6.1%
5 160168
 
5.9%
0 138820
 
5.1%
Other values (8) 716985
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1367576
50.0%
Decimal Number 1367576
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 187685
13.7%
2 182588
13.4%
4 174978
12.8%
1 167614
12.3%
5 160168
11.7%
0 138820
10.2%
7 104302
7.6%
6 97530
7.1%
8 97514
7.1%
9 56377
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
N 316193
23.1%
B 262277
19.2%
Q 250524
18.3%
K 177320
13.0%
S 105318
 
7.7%
I 105318
 
7.7%
X 84957
 
6.2%
M 65669
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1367576
50.0%
Common 1367576
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 187685
13.7%
2 182588
13.4%
4 174978
12.8%
1 167614
12.3%
5 160168
11.7%
0 138820
10.2%
7 104302
7.6%
6 97530
7.1%
8 97514
7.1%
9 56377
 
4.1%
Latin
ValueCountFrequency (%)
N 316193
23.1%
B 262277
19.2%
Q 250524
18.3%
K 177320
13.0%
S 105318
 
7.7%
I 105318
 
7.7%
X 84957
 
6.2%
M 65669
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2735152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 316193
11.6%
B 262277
 
9.6%
Q 250524
 
9.2%
3 187685
 
6.9%
2 182588
 
6.7%
K 177320
 
6.5%
4 174978
 
6.4%
1 167614
 
6.1%
5 160168
 
5.9%
0 138820
 
5.1%
Other values (8) 716985
26.2%
Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:45.462899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length56
Median length39
Mean length19.836797
Min length6

Characters and Unicode

Total characters13564164
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKew Gardens Hills
2nd rowJackson Heights
3rd rowBaisley Park
4th rowBay Ridge
5th rowHammels-Arverne-Edgemere
ValueCountFrequency (%)
park 55089
 
3.7%
east 49319
 
3.3%
heights 48397
 
3.2%
hill 34811
 
2.3%
new 34005
 
2.3%
south 30523
 
2.0%
north 29852
 
2.0%
beach 29126
 
2.0%
hills 28352
 
1.9%
village 26269
 
1.8%
Other values (250) 1126691
75.5%
2023-12-13T09:38:45.903566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1215359
 
9.0%
a 963637
 
7.1%
l 880073
 
6.5%
o 853763
 
6.3%
808646
 
6.0%
r 804239
 
5.9%
i 767966
 
5.7%
n 738160
 
5.4%
t 732792
 
5.4%
s 708887
 
5.2%
Other values (45) 5090642
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10289513
75.9%
Uppercase Letter 1967877
 
14.5%
Space Separator 808646
 
6.0%
Dash Punctuation 459153
 
3.4%
Other Punctuation 32993
 
0.2%
Open Punctuation 2991
 
< 0.1%
Close Punctuation 2991
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1215359
11.8%
a 963637
9.4%
l 880073
 
8.6%
o 853763
 
8.3%
r 804239
 
7.8%
i 767966
 
7.5%
n 738160
 
7.2%
t 732792
 
7.1%
s 708887
 
6.9%
d 403036
 
3.9%
Other values (15) 2221601
21.6%
Uppercase Letter
ValueCountFrequency (%)
H 254261
12.9%
B 226733
11.5%
S 160874
 
8.2%
P 154900
 
7.9%
C 127859
 
6.5%
M 108390
 
5.5%
G 96399
 
4.9%
E 95949
 
4.9%
N 95605
 
4.9%
W 94221
 
4.8%
Other values (14) 552686
28.1%
Other Punctuation
ValueCountFrequency (%)
' 16745
50.8%
. 16248
49.2%
Space Separator
ValueCountFrequency (%)
808646
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 459153
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2991
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12257390
90.4%
Common 1306774
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1215359
 
9.9%
a 963637
 
7.9%
l 880073
 
7.2%
o 853763
 
7.0%
r 804239
 
6.6%
i 767966
 
6.3%
n 738160
 
6.0%
t 732792
 
6.0%
s 708887
 
5.8%
d 403036
 
3.3%
Other values (39) 4189478
34.2%
Common
ValueCountFrequency (%)
808646
61.9%
- 459153
35.1%
' 16745
 
1.3%
. 16248
 
1.2%
( 2991
 
0.2%
) 2991
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13564164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1215359
 
9.0%
a 963637
 
7.1%
l 880073
 
6.5%
o 853763
 
6.3%
808646
 
6.0%
r 804239
 
5.9%
i 767966
 
5.7%
n 738160
 
5.4%
t 732792
 
5.4%
s 708887
 
5.2%
Other values (45) 5090642
37.5%

boro_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct2152
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3404914.1
Minimum1000201
Maximum5032300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:46.089788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1000201
5-th percentile1015700
Q13011700
median4008100
Q34103202
95-th percentile5019800
Maximum5032300
Range4032099
Interquartile range (IQR)1091502

Descriptive statistics

Standard deviation1175863.4
Coefficient of variation (CV)0.34534305
Kurtosis-0.53896426
Mean3404914.1
Median Absolute Deviation (MAD)964100
Skewness-0.55026943
Sum2.3282394 × 1012
Variance1.3826548 × 1012
MonotonicityNot monotonic
2023-12-13T09:38:46.281444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5020801 3776
 
0.6%
5017600 3245
 
0.5%
5020803 2749
 
0.4%
5020804 2723
 
0.4%
5019800 2620
 
0.4%
5022600 2452
 
0.4%
5017005 2402
 
0.4%
4089200 2399
 
0.4%
5024401 2392
 
0.3%
5017010 2269
 
0.3%
Other values (2142) 656761
96.0%
ValueCountFrequency (%)
1000201 70
 
< 0.1%
1000202 217
< 0.1%
1000600 187
< 0.1%
1000700 144
< 0.1%
1000800 288
< 0.1%
1000900 83
 
< 0.1%
1001001 24
 
< 0.1%
1001002 56
 
< 0.1%
1001200 111
 
< 0.1%
1001300 94
 
< 0.1%
ValueCountFrequency (%)
5032300 114
 
< 0.1%
5031902 511
 
0.1%
5031901 393
 
0.1%
5030302 769
0.1%
5030301 572
 
0.1%
5029104 1294
0.2%
5029103 1636
0.2%
5029102 752
0.1%
5027900 416
 
0.1%
5027706 529
 
0.1%

state
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
New York
683788 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5470304
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowNew York

Common Values

ValueCountFrequency (%)
New York 683788
100.0%

Length

2023-12-13T09:38:46.476754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:38:46.608263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
new 683788
50.0%
york 683788
50.0%

Most occurring characters

ValueCountFrequency (%)
N 683788
12.5%
e 683788
12.5%
w 683788
12.5%
683788
12.5%
Y 683788
12.5%
o 683788
12.5%
r 683788
12.5%
k 683788
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3418940
62.5%
Uppercase Letter 1367576
 
25.0%
Space Separator 683788
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 683788
20.0%
w 683788
20.0%
o 683788
20.0%
r 683788
20.0%
k 683788
20.0%
Uppercase Letter
ValueCountFrequency (%)
N 683788
50.0%
Y 683788
50.0%
Space Separator
ValueCountFrequency (%)
683788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4786516
87.5%
Common 683788
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 683788
14.3%
e 683788
14.3%
w 683788
14.3%
Y 683788
14.3%
o 683788
14.3%
r 683788
14.3%
k 683788
14.3%
Common
ValueCountFrequency (%)
683788
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5470304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 683788
12.5%
e 683788
12.5%
w 683788
12.5%
683788
12.5%
Y 683788
12.5%
o 683788
12.5%
r 683788
12.5%
k 683788
12.5%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct676080
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.701261
Minimum40.498466
Maximum40.912918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:46.744982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum40.498466
5-th percentile40.548447
Q140.631928
median40.700612
Q340.762228
95-th percentile40.856407
Maximum40.912918
Range0.41445217
Interquartile range (IQR)0.13029949

Descriptive statistics

Standard deviation0.090311355
Coefficient of variation (CV)0.0022188834
Kurtosis-0.63383411
Mean40.701261
Median Absolute Deviation (MAD)0.0650372
Skewness0.062738079
Sum27831034
Variance0.0081561409
MonotonicityNot monotonic
2023-12-13T09:38:46.918155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.66035938 35
 
< 0.1%
40.61097709 28
 
< 0.1%
40.61596593 17
 
< 0.1%
40.689461 17
 
< 0.1%
40.77849693 11
 
< 0.1%
40.66036234 9
 
< 0.1%
40.8830341 9
 
< 0.1%
40.85571674 8
 
< 0.1%
40.7715108 8
 
< 0.1%
40.78750377 7
 
< 0.1%
Other values (676070) 683639
> 99.9%
ValueCountFrequency (%)
40.49846614 1
< 0.1%
40.49847126 1
< 0.1%
40.49850958 1
< 0.1%
40.49854295 1
< 0.1%
40.4985987 1
< 0.1%
40.49874956 1
< 0.1%
40.49879352 1
< 0.1%
40.49881226 1
< 0.1%
40.49881678 1
< 0.1%
40.49887148 1
< 0.1%
ValueCountFrequency (%)
40.91291831 1
< 0.1%
40.91280676 1
< 0.1%
40.91271785 1
< 0.1%
40.91261439 1
< 0.1%
40.91260541 1
< 0.1%
40.9124346 1
< 0.1%
40.91236829 1
< 0.1%
40.91220869 1
< 0.1%
40.91217396 1
< 0.1%
40.91215184 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct677101
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.92406
Minimum-74.254965
Maximum-73.700488
Zeros0
Zeros (%)0.0%
Negative683788
Negative (%)100.0%
Memory size5.2 MiB
2023-12-13T09:38:47.115137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-74.254965
5-th percentile-74.169931
Q1-73.9805
median-73.912911
Q3-73.83491
95-th percentile-73.744469
Maximum-73.700488
Range0.55447653
Interquartile range (IQR)0.1455898

Descriptive statistics

Standard deviation0.12358346
Coefficient of variation (CV)-0.0016717623
Kurtosis-0.11879602
Mean-73.92406
Median Absolute Deviation (MAD)0.071501375
Skewness-0.61207924
Sum-50548385
Variance0.015272871
MonotonicityNot monotonic
2023-12-13T09:38:47.294954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.76103529 35
 
< 0.1%
-74.15686098 29
 
< 0.1%
-73.75274984 17
 
< 0.1%
-73.95680062 17
 
< 0.1%
-73.79764749 11
 
< 0.1%
-73.90045375 9
 
< 0.1%
-73.76106931 9
 
< 0.1%
-73.91441448 8
 
< 0.1%
-73.91666671 8
 
< 0.1%
-73.85143651 7
 
< 0.1%
Other values (677091) 683638
> 99.9%
ValueCountFrequency (%)
-74.2549647 1
< 0.1%
-74.25489452 1
< 0.1%
-74.25487627 1
< 0.1%
-74.25485662 1
< 0.1%
-74.25483416 1
< 0.1%
-74.25479222 1
< 0.1%
-74.25477504 1
< 0.1%
-74.2547486 1
< 0.1%
-74.25472217 1
< 0.1%
-74.25469573 1
< 0.1%
ValueCountFrequency (%)
-73.70048817 1
< 0.1%
-73.70059248 1
< 0.1%
-73.70059368 1
< 0.1%
-73.70059651 1
< 0.1%
-73.70060124 1
< 0.1%
-73.7006054 1
< 0.1%
-73.70060622 1
< 0.1%
-73.70061034 1
< 0.1%
-73.70062065 1
< 0.1%
-73.70064005 1
< 0.1%

x_sp
Real number (ℝ)

HIGH CORRELATION 

Distinct681630
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005279.9
Minimum913349.27
Maximum1067247.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:47.510377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum913349.27
5-th percentile937034.4
Q1989657.84
median1008386.2
Q31029991.3
95-th percentile1055117.1
Maximum1067247.6
Range153898.36
Interquartile range (IQR)40333.437

Descriptive statistics

Standard deviation34285.054
Coefficient of variation (CV)0.034104985
Kurtosis-0.11379428
Mean1005279.9
Median Absolute Deviation (MAD)19812.179
Skewness-0.61435668
Sum6.8739831 × 1011
Variance1.175465 × 109
MonotonicityNot monotonic
2023-12-13T09:38:47.692423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1050549.648 35
 
< 0.1%
940697.4014 28
 
< 0.1%
996243.4593 17
 
< 0.1%
1052818.477 17
 
< 0.1%
1040292.358 11
 
< 0.1%
1014131.162 9
 
< 0.1%
1050540.207 9
 
< 0.1%
1011776.478 9
 
< 0.1%
1007955.765 8
 
< 0.1%
1007302.748 8
 
< 0.1%
Other values (681620) 683637
> 99.9%
ValueCountFrequency (%)
913349.2661 1
< 0.1%
913368.6477 1
< 0.1%
913373.6867 1
< 0.1%
913379.1135 1
< 0.1%
913385.3156 1
< 0.1%
913396.8953 1
< 0.1%
913401.6387 1
< 0.1%
913408.9363 1
< 0.1%
913416.2338 1
< 0.1%
913423.5311 1
< 0.1%
ValueCountFrequency (%)
1067247.624 1
< 0.1%
1067220.126 1
< 0.1%
1067219.309 1
< 0.1%
1067218.901 1
< 0.1%
1067217.945 1
< 0.1%
1067216.745 1
< 0.1%
1067215.32 1
< 0.1%
1067214.942 1
< 0.1%
1067212.348 1
< 0.1%
1067206.751 1
< 0.1%

y_sp
Real number (ℝ)

HIGH CORRELATION 

Distinct682632
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194798.42
Minimum120973.79
Maximum271894.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-12-13T09:38:47.870712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum120973.79
5-th percentile139145.45
Q1169515.15
median194560.25
Q3217019.57
95-th percentile251311.16
Maximum271894.09
Range150920.3
Interquartile range (IQR)47504.418

Descriptive statistics

Standard deviation32902.061
Coefficient of variation (CV)0.16890312
Kurtosis-0.63530824
Mean194798.42
Median Absolute Deviation (MAD)23708.383
Skewness0.062752468
Sum1.3320083 × 1011
Variance1.0825456 × 109
MonotonicityNot monotonic
2023-12-13T09:38:48.350783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179953.5509 35
 
< 0.1%
161910.8114 28
 
< 0.1%
190562.4395 17
 
< 0.1%
163692.3397 17
 
< 0.1%
222968.9875 11
 
< 0.1%
261006.6421 9
 
< 0.1%
179954.6024 9
 
< 0.1%
251049.181 8
 
< 0.1%
220370.5573 8
 
< 0.1%
202587.9351 7
 
< 0.1%
Other values (682622) 683639
> 99.9%
ValueCountFrequency (%)
120973.7922 1
< 0.1%
120974.9307 1
< 0.1%
120989.6301 1
< 0.1%
121001.0648 1
< 0.1%
121021.3909 1
< 0.1%
121077.124 1
< 0.1%
121093.1548 1
< 0.1%
121098.6109 1
< 0.1%
121100.9996 1
< 0.1%
121122.1262 1
< 0.1%
ValueCountFrequency (%)
271894.0921 1
< 0.1%
271853.4435 1
< 0.1%
271821.042 1
< 0.1%
271783.3394 1
< 0.1%
271780.3538 1
< 0.1%
271718.4015 1
< 0.1%
271694.1909 1
< 0.1%
271635.8193 1
< 0.1%
271623.217 1
< 0.1%
271615.2311 1
< 0.1%

Interactions

2023-12-13T09:38:07.204119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:07.346557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:12.196657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:16.707453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:21.155729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:25.517161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:29.992814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:34.363061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:39.107773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:43.480689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:48.420258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:53.387620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:57.750048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:02.101491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:07.588924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:08.070255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:12.516618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:16.996403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:21.531295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:25.914890image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:30.297294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:34.688252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:39.452041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:43.998184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:48.769434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:53.728563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:58.016536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:02.585457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:07.958817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:08.395045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:12.844059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:17.321681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:21.807566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:26.188790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:30.651167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:34.989475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:39.808537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:44.264087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:49.140793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:53.987556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:58.319630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:02.845051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:08.249577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:08.691674image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:13.109902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:17.652040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:22.073920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:26.486439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:30.935148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:35.234864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:40.171983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:44.600682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:49.517463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:54.290918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:58.604812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:03.193854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:08.597352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:09.049659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:13.386816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:17.951652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:22.415407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:26.777532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:31.270710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:35.526635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:40.526651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:44.944529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:49.928918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:54.541648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:58.956823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:03.542994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:08.951788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:09.368601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:13.734866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:18.260303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:22.715506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:27.032387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:31.560449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:35.937308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:40.827282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:45.295584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:50.283135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:54.837061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:59.250977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:03.901753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:09.243804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:09.642863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:14.050967image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:18.629501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:23.070878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:27.344684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:31.905667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:36.248856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:41.124439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:45.641671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:50.724204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:55.118561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:59.540667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:04.295069image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:09.612407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:09.938817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:14.391756image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:18.945093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:23.316653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:27.799198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:32.169947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:36.570156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:41.375656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:45.949888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:51.061083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:55.401183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:59.858564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:04.657362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:09.976573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:10.279958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:14.706589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:19.272851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:23.647847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:28.092013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:32.523852image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:36.911895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:41.741741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:46.315262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:51.420922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:55.738909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:00.283145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:05.017430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:10.271474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:10.737324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:14.996552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:19.618032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:23.983945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:28.455043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:32.797022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:37.251274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:42.023154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:46.646600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:51.730598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:56.083094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:00.635014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:05.402117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:10.606675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:11.051456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:15.292339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:19.933905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:24.352049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:28.746727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:33.097745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:37.644033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:42.322541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:47.030777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:52.042170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:56.410082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:00.970071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:05.789311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:10.931902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:11.344191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:15.648614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:20.221279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:24.652888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:29.021628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:33.396775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:38.016720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:42.572066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:47.336971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:52.421107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:56.734731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:01.232772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:06.117773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:11.309849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:11.628515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:15.955096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:20.499225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:24.964545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:29.350484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:33.728225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:38.352386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:42.865550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:47.721897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:52.736763image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:57.090704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:01.522860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:06.420477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:11.684767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:11.922117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:16.330848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:20.842935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:25.233455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:29.670535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:34.039180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:38.714809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:43.157381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:48.064651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:53.076643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:37:57.430116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:01.792822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-12-13T09:38:06.800777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-12-13T09:38:48.614248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
block_idboro_ctborocodeboronamebrch_lightbrch_otherbrch_shoecb_numcncldistcurb_locguardshealthlatitudelongituderoot_grateroot_otherroot_stonesidewalkst_assemst_senatestatusstewardstump_diamtree_dbhtrnk_lighttrnk_othertrunk_wireuser_typex_spy_spzip_cityzipcodetree_id
block_id1.0000.4000.9960.9960.1110.1280.0140.4120.0530.0960.2080.0340.0750.1210.1490.0780.1460.0940.1740.1320.0330.1550.008-0.0050.0300.0870.0360.3510.1210.0760.814-0.0250.082
boro_ct0.4001.0000.9990.9990.1140.1270.0140.9520.5570.0470.2110.033-0.5120.0410.1490.0780.1460.088-0.486-0.5290.0330.1550.0110.0800.0300.0860.0370.3610.041-0.5120.8930.2650.201
borocode0.9960.9991.0001.0000.1100.1270.0140.9630.5930.0470.2070.033-0.549-0.0260.1490.0780.1450.088-0.483-0.5340.0330.1530.0100.0770.0300.0870.0350.353-0.026-0.5491.0000.2790.203
boroname0.9960.9991.0001.0000.1100.1270.0140.8530.3710.0470.2070.033-0.430-0.0920.1490.0780.1450.088-0.397-0.4540.0330.1530.0050.0480.0300.0870.0350.353-0.091-0.4301.0000.0890.111
brch_light0.1110.1140.1100.1101.0000.0030.0090.0670.0290.0420.0540.025-0.0120.0610.0110.0580.1430.110-0.061-0.0740.0700.038-0.0520.1740.0630.0490.1750.0630.062-0.0120.1690.0870.011
brch_other0.1280.1270.1270.1270.0031.0000.012-0.104-0.0670.0060.0700.1520.050-0.0330.0390.1650.0590.0510.0470.0570.0420.070-0.0310.0140.0120.2300.0320.137-0.0330.0500.136-0.055-0.083
brch_shoe0.0140.0140.0140.0140.0090.0121.000-0.016-0.0080.0020.0020.0070.009-0.0030.0030.0090.0170.0090.0090.0080.0050.000-0.0040.0110.0040.0140.0040.012-0.0030.0090.017-0.005-0.009
cb_num0.4120.9520.9630.8530.067-0.104-0.0161.0000.5990.0470.2100.033-0.5990.0410.1490.0800.1450.089-0.485-0.5350.0340.1560.0150.0900.0310.0870.0370.3720.041-0.5990.8940.3120.242
cncldist0.0530.5570.5930.3710.029-0.067-0.0080.5991.0000.0540.2160.037-0.929-0.5190.1780.0860.1360.081-0.108-0.1240.0300.170-0.0040.0430.0290.0930.0430.381-0.518-0.9290.7190.0180.135
curb_loc0.0960.0470.0470.0470.0420.0060.0020.0470.0541.0000.0350.006-0.0090.0140.0100.0140.0320.066-0.025-0.0300.0090.0210.008-0.0440.0000.0000.0150.0260.014-0.0090.0940.0290.020
guards0.2080.2110.2070.2070.0540.0700.0020.2100.2160.0351.0000.021-0.0860.1390.0550.0880.0930.048-0.144-0.1661.0000.324NaN0.0840.0320.0600.0140.1680.139-0.0860.2200.1720.133
health0.0340.0330.0330.0330.0250.1520.0070.0330.0370.0060.0211.0000.0020.0090.0230.0540.0300.020-0.001-0.0041.0000.008NaN-0.0070.0080.1350.0280.0250.0090.0020.0740.0020.072
latitude0.075-0.512-0.549-0.430-0.0120.0500.009-0.599-0.929-0.009-0.0860.0021.0000.5110.0800.0590.1140.0790.1530.1650.0260.0610.000-0.0350.0160.0720.0320.2860.5101.0000.585-0.028-0.136
longitude0.1210.041-0.026-0.0920.061-0.033-0.0030.041-0.5190.0140.1390.0090.5111.0000.0850.0810.1550.092-0.554-0.5510.0270.1360.0280.0800.0210.0920.0310.3781.0000.5110.6360.7140.192
root_grate0.1490.1490.1490.1490.0110.0390.0030.1490.1780.0100.0550.0230.0800.0851.0000.0230.0150.0010.0640.0600.0160.016-0.012-0.0080.0350.0370.0070.061-0.0400.0490.153-0.078-0.045
root_other0.0780.0780.0780.0780.0580.1650.0090.0800.0860.0140.0880.0540.0590.0810.0231.0000.0560.0880.0200.0310.0470.023-0.0350.0890.0180.2040.0500.104-0.0170.0390.101-0.021-0.068
root_stone0.1460.1460.1450.1450.1430.0590.0170.1450.1360.0320.0930.0300.1140.1550.0150.0561.0000.344-0.045-0.0310.1120.106-0.0830.3370.0080.0790.0510.1690.0240.0500.1970.072-0.031
sidewalk0.0940.0880.0880.0880.1100.0510.0090.0890.0810.0660.0480.0200.0790.0920.0010.0880.3441.0000.0080.0041.0000.057NaN-0.2530.0000.0650.0350.0830.001-0.0070.131-0.0340.001
st_assem0.174-0.486-0.483-0.397-0.0610.0470.009-0.485-0.108-0.025-0.144-0.0010.153-0.5540.0640.020-0.0450.0081.0000.9120.0370.120-0.032-0.1290.0270.0780.0420.329-0.5540.1520.670-0.838-0.219
st_senate0.132-0.529-0.534-0.454-0.0740.0570.008-0.535-0.124-0.030-0.166-0.0040.165-0.5510.0600.031-0.0310.0040.9121.0000.0380.149-0.028-0.1230.0270.0710.0330.299-0.5520.1650.628-0.792-0.247
status0.0330.0330.0330.0330.0700.0420.0050.0340.0300.0091.0001.0000.0260.0270.0160.0470.1121.0000.0370.0381.0001.0000.755-0.2900.0080.0490.0310.0080.0200.0120.0430.018-0.010
steward0.1550.1550.1530.1530.0380.0700.0000.1560.1700.0210.3240.0080.0610.1360.0160.0230.1060.0570.1200.1491.0001.000NaN0.2460.0340.0360.0130.1520.124-0.0330.1720.1190.141
stump_diam0.0080.0110.0100.005-0.052-0.031-0.0040.015-0.0040.008NaNNaN0.0000.028-0.012-0.035-0.083NaN-0.032-0.0280.755NaN1.000-0.2750.0030.0250.0160.0130.0280.0000.0210.0370.002
tree_dbh-0.0050.0800.0770.0480.1740.0140.0110.0900.043-0.0440.084-0.007-0.0350.080-0.0080.0890.337-0.253-0.129-0.123-0.2900.246-0.2751.0000.0000.0010.0060.0060.080-0.0350.0070.1190.087
trnk_light0.0300.0300.0300.0300.0630.0120.0040.0310.0290.0000.0320.0080.0160.0210.0350.0180.0080.0000.0270.0270.0080.0340.0030.0001.0000.0100.0480.020-0.0110.0110.035-0.015-0.014
trnk_other0.0870.0860.0870.0870.0490.2300.0140.0870.0930.0000.0600.1350.0720.0920.0370.2040.0790.0650.0780.0710.0490.0360.0250.0010.0101.0000.0420.154-0.0100.0460.153-0.012-0.065
trunk_wire0.0360.0370.0350.0350.1750.0320.0040.0370.0430.0150.0140.0280.0320.0310.0070.0500.0510.0350.0420.0330.0310.0130.0160.0060.0480.0421.0000.0480.0110.0020.0510.020-0.010
user_type0.3510.3610.3530.3530.0630.1370.0120.3720.3810.0260.1680.0250.2860.3780.0610.1040.1690.0830.3290.2990.0080.1520.0130.0060.0200.1540.0481.0000.0490.2400.4500.100-0.258
x_sp0.1210.041-0.026-0.0910.062-0.033-0.0030.041-0.5180.0140.1390.0090.5101.000-0.040-0.0170.0240.001-0.554-0.5520.0200.1240.0280.080-0.011-0.0100.0110.0491.0000.5110.6360.7150.192
y_sp0.076-0.512-0.549-0.430-0.0120.0500.009-0.599-0.929-0.009-0.0860.0021.0000.5110.0490.0390.050-0.0070.1520.1650.012-0.0330.000-0.0350.0110.0460.0020.2400.5111.0000.585-0.027-0.136
zip_city0.8140.8931.0001.0000.1690.1360.0170.8940.7190.0940.2200.0740.5850.6360.1530.1010.1970.1310.6700.6280.0430.1720.0210.0070.0350.1530.0510.4500.6360.5851.000-0.0900.073
zipcode-0.0250.2650.2790.0890.087-0.055-0.0050.3120.0180.0290.1720.002-0.0280.714-0.078-0.0210.072-0.034-0.838-0.7920.0180.1190.0370.119-0.015-0.0120.0200.1000.715-0.027-0.0901.0000.267
tree_id0.0820.2010.2030.1110.011-0.083-0.0090.2420.1350.0200.1330.072-0.1360.192-0.045-0.068-0.0310.001-0.219-0.247-0.0100.1410.0020.087-0.014-0.065-0.010-0.2580.192-0.1360.0730.2671.000

Missing values

2023-12-13T09:38:14.652976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:38:19.936974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T09:38:27.544684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

tree_idblock_idcreated_attree_dbhstump_diamcurb_locstatushealthspc_latinspc_commonstewardguardssidewalkuser_typeproblemsroot_stoneroot_grateroot_othertrunk_wiretrnk_lighttrnk_otherbrch_lightbrch_shoebrch_otheraddresszipcodezip_citycb_numborocodeboronamecncldistst_assemst_senatentanta_nameboro_ctstatelatitudelongitudex_spy_sp
06069453057782016-06-28100OnCurbAliveGoodFraxinus pennsylvanicagreen ashNoneNoneNoDamageTreesCount StaffStonesYesNoNoNoNoNoNoNoNo76-046 164 STREET11366Fresh Meadows4084Queens242514QN37Kew Gardens Hills4125700New York40.724339-73.8051801.038250e+06203232.9417
11603213412732015-08-1990OnCurbAliveGoodGleditsia triacanthos var. inermishoneylocustNoneNoneNoDamageVolunteerBranchLightsNoNoNoNoNoNoYesNoNo72-020 32 AVENUE11370East Elmhurst4034Queens253413QN28Jackson Heights4030902New York40.756626-73.8941671.013571e+06214953.6472
25413473252812015-12-3070OnCurbAliveGoodPyrus calleryanaCallery pearNoneNoneNoDamageTreesCount StaffBranchLightsNoNoNoNoNoNoYesNoNo153-026 119 AVENUE11434Jamaica4124Queens283210QN76Baisley Park4028800New York40.679777-73.7884631.042923e+06187008.2671
36139302038222016-07-05100OnCurbAliveGoodPyrus calleryanaCallery pearNoneNoneNoDamageTreesCount StaffNoneNoNoNoNoNoNoNoNoNo89 89 STREET11209Brooklyn3103Brooklyn434622BK31Bay Ridge3005000New York40.622743-74.0375439.738279e+05166160.5847
4183533389112015-06-1340OnCurbAliveGoodPrunus virginiana'Schubert' chokecherryNoneNoneNoDamageTreesCount StaffBranchLightsNoNoNoNoNoNoYesNoNo559 BEACH 68 STREET11692Arverne4144Queens313110QN12Hammels-Arverne-Edgemere4095400New York40.596514-73.7976221.040452e+06156667.5017
5211731087132015-06-1580OnCurbAliveGoodGleditsia triacanthos var. inermishoneylocustNoneNoneNoDamageVolunteerTrunkOtherBranchOtherNoNoNoNoNoYesNoNoYes3554 BROADWAY10031New York1091Manhattan77131MN04Hamilton Heights1022900New York40.826887-73.9498899.981185e+05240538.5367
65446982014342016-01-2020OnCurbAliveFairQuercus rubranorthern red oak1or2NoneNoDamageTreesCount StaffNoneNoNoNoNoNoNoNoNoNo2030 PITKIN AVENUE11207Brooklyn3053Brooklyn425519BK85East New York (Pennsylvania Ave)3114400New York40.671347-73.8976141.012652e+06183882.7143
75462402287782016-02-0620OnCurbAliveGoodTilia americanaAmerican linden1or2HelpfulNoDamageVolunteerNoneNoNoNoNoNoNoNoNoNo5008 FT HAMILTON PARKWAY11219Brooklyn3123Brooklyn444817BK88Borough Park3011400New York40.637774-73.9986929.846129e+05171634.7857
86463483097292016-07-2940OnCurbAliveGoodQuercus palustrispin oakNoneNoneDamageTreesCount StaffNoneNoNoNoNoNoNoNoNoNo85-006 WOODHAVEN BOULEVARD11421Woodhaven4094Queens323815QN53Woodhaven4001400New York40.696668-73.8530871.024988e+06193125.4947
94138125011962015-11-0250OnCurbAliveGoodUlmus americanaAmerican elmNoneNoneDamageTreesCount StaffNoneNoNoNoNoNoNoNoNoNo1340 EAST BAY AVENUE10474Bronx2022Bronx178434BX27Hunts Point2009300New York40.808967-73.8826471.016736e+06234027.3437
tree_idblock_idcreated_attree_dbhstump_diamcurb_locstatushealthspc_latinspc_commonstewardguardssidewalkuser_typeproblemsroot_stoneroot_grateroot_othertrunk_wiretrnk_lighttrnk_otherbrch_lightbrch_shoebrch_otheraddresszipcodezip_citycb_numborocodeboronamecncldistst_assemst_senatentanta_nameboro_ctstatelatitudelongitudex_spy_sp
6837784476565028432015-11-1110OnCurbAliveGoodZelkova serrataJapanese zelkovaNoneNoneNoDamageNYC Parks StaffNoneNoNoNoNoNoNoNoNoNo610 RIVER AVENUE10451Bronx2042Bronx87729BX63West Concourse2006300New York40.821540-73.9293411.003807e+06238594.3657
6837795725833009422016-06-0160OnCurbAliveGoodCornus masCornelian cherryNoneNoneNoDamageVolunteerNoneNoNoNoNoNoNoNoNoNo28-048 46 STREET11103Astoria4014Queens223612QN70Astoria4014700New York40.762035-73.9099371.009200e+06216919.2837
6837806285273172472016-07-15110OnCurbAliveGoodGinkgo bilobaginkgoNoneNoneNoDamageTreesCount StaffStonesTrunkOtherYesNoNoNoNoYesNoNoNo114-016 150 AVENUE11420South Ozone Park4104Queens323115QN55South Ozone Park4084601New York40.667726-73.8269591.032254e+06182594.3356
6837813092544107152015-10-1170OnCurbAliveGoodAcer platanoides 'Crimson King'crimson king mapleNoneNoneNoDamageTreesCount StaffStonesYesNoNoNoNoNoNoNoNo310 KINGHORN STREET10312Staten Island5035Staten Island516224SI01Annadale-Huguenot-Prince's Bay-Eltingville5017600New York40.528899-74.1681059.375181e+05132013.5133
6837821716641031742015-08-24120OnCurbAliveGoodTilia americanaAmerican linden1or2HelpfulNoDamageVolunteerNoneNoNoNoNoNoNoNoNoNo205 AVENUE C10009New York1031Manhattan27427MN28Lower East Side1002800New York40.727348-73.9765739.907431e+05204270.0951
6837832377882233442015-09-1920OnCurbAlivePoorPrunus cerasiferapurple-leaf plum1or2NoneNoDamageTreesCount StaffNoneNoNoNoNoNoNoNoNoNo1 BEARD STREET11231Brooklyn3063Brooklyn385125BK33Carroll Gardens-Columbia Street-Red Hook3005300New York40.672566-74.0114739.810674e+05184310.4162
6837842494893353142015-09-2320OnCurbDeadNaNNaNNaNNaNNaNNaNNYC Parks StaffNaNNoNoNoNoNoNoNoNoNo87-015 LITTLE NECK PARKWAY11001Floral Park4134Queens233311QN44Glen Oaks-Floral Park-New Hyde Park4157903New York40.730434-73.7106001.064458e+06205525.7957
6837852302612303032015-09-1620OnCurbDeadNaNNaNNaNNaNNaNNaNTreesCount StaffNaNNoNoNoNoNoNoNoNoNo644 EAST 8 STREET11230Brooklyn3143Brooklyn404417BK42Flatbush3048200New York40.633890-73.9697799.926380e+05170220.9185
6837866237843183682016-07-12180OnCurbAliveGoodQuercus rubranorthern red oakNoneNoneDamageNYC Parks StaffNoneNoNoNoNoNoNoNoNoNo116-019 125 STREET11420South Ozone Park4104Queens283110QN55South Ozone Park4017800New York40.676190-73.8131351.036082e+06185685.7796
6837871397492178362015-08-12110OnCurbAliveGoodZelkova serrataJapanese zelkovaNoneNoneNoDamageVolunteerNoneNoNoNoNoNoNoNoNoNo209 SOUTH 2 STREET11211Brooklyn3013Brooklyn345318BK73North Side-South Side3052300New York40.712328-73.9593859.955098e+05198799.3400